class_docstr: Add gaussian noise to a batch of multi-dimensional images.
  
  .. image:: _static/img/RandomGaussianNoise.png
  
  Args:
      mean: The mean of the gaussian distribution.
      std: The standard deviation of the gaussian distribution.
      same_on_batch: apply the same transformation across the batch.
      p: probability of applying the transformation.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> img = torch.ones(1, 1, 2, 2)
      >>> RandomGaussianNoise(mean=0., std=1., p=1.)(img)
      tensor([[[[ 2.5410,  0.7066],
                [-1.1788,  1.5684]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomGaussianNoise(mean=0., std=1., p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def __init__(
      self,
      mean: float = 0.0,
      std: float = 1.0,
      same_on_batch: bool = False,
      p: float = 0.5,
      keepdim: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim
      )
      self.flags = {"mean": mean, "std": std}
  
file_path: kornia/kornia/augmentation/_2d/intensity/gaussian_noise.py
imports: from typing import Any, Dict, Optional, Tuple
  import torch
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.core import Tensor
label_desc: Use image node_id 1 to create a new RandomGaussianNoise object from inherited base classes: IntensityAugmentationBase2D with example: obj = RandomGaussianNoise(mean, std, same_on_batch, p, keepdim)